Efficient data classification method and apparatus based on dictionary contrastive learning via adaptive label embedding

Abstract

Proposed are a data classification method and apparatus. The data classification method that is performed by the data classification apparatus includes extracting features from input data through a learning network model and outputting prediction results based on the features, and the learning network model compares local features derived through an individual layer other than the final layer of the learning network model with label embedding vectors corresponding to a classification label.

What is claimed is: 
     
         1 . A data classification method, the data classification method being performed by a data classification apparatus, the data classification method comprising extracting features from input data through a learning network model and outputting prediction results based on the features;
 wherein the learning network model compares local features derived through an individual layer other than a final layer of the learning network model with label embedding vectors corresponding to a classification label.   
     
     
         2 . The data classification method of  claim 1 , wherein the learning network model is set to prevent error signals of local features, derived from at least one layer, from being propagated in a direction of a previous layer by removing dependency on an operation graph used for gradient calculation so that an operation value processed by at least one layer of the learning network model cannot be tracked. 
     
     
         3 . The data classification method of  claim 1 , wherein the learning network model directly compares the label embedding vectors and the local features by using a label embedding dictionary which is connected to at least one layer of the learning network model and in which the label embedding vectors are mapped. 
     
     
         4 . The data classification method of  claim 3 , wherein the label embedding vectors of the label embedding dictionary are adaptively and dynamically updated based on the error signals of the local features. 
     
     
         5 . The data classification method of  claim 3 , wherein at least one layer of the learning network model receives the error signals of the local features from a loss function set based on dictionary contrastive learning. 
     
     
         6 . The data classification method of  claim 5 , wherein parameters of the learning network model are updated in order to maximize similarity between label embedding vectors corresponding to the local features in the label embedding dictionary and the local features while minimizing similarity between label embedding vectors not corresponding to the local features in the label embedding dictionary and the local features. 
     
     
         7 . The data classification method of  claim 3 , wherein the learning network model is a model which calculates a final error signal for the final layer of the learning network model and in which a backpropagation path between an immediately previous layer of the final layer and the final layer is detached so that the final error signal is not propagated to an intermediate layer of the learning network model. 
     
     
         8 . A data classification apparatus, comprising:
 memory configured to store a learning network model having a plurality of layers; and   a controller configured to extract features from input data through the learning network model and output prediction results based on the features;   wherein the learning network model compares local features derived through an individual layer other than a final layer of the learning network model with label embedding vectors corresponding to a classification label.   
     
     
         9 . A non-transitory computer-readable storage medium having stored thereon a program that, when executed by a processor, causes the processor to execute the method set forth in  claim 1 . 
     
     
         10 . A computer program that is executed by a data classification apparatus and stored in a non-transitory computer-readable storage medium to perform the method set forth in  claim 1 .